Abstract:

The neocortex is a continuous sheet composed of rather stereotypical local
microcircuits that consist of neurons on several laminae with characteristic
synaptic connectivity patterns. An understanding of the structure and
computational function of these cortical microcircuits may hold the key for
understanding the enormous computational power of the neocortex. Two
templates for the structure of laminar cortical microcircuits have recently
been published by Thomson et al. (2002) and Binzegger et al. (2004), both
resulting from long-lasting experimental studies (but based on different
methods). We analyze and compare in this study the structure and
computational properties of these two microcircuit templates. In particular,
we examine the distribution of network motifs, i.e. of sub-circuits
consisting of a small number of neurons. The distribution of these building
blocks of complex networks has recently emerged as a method for
characterizing similarities and differences among complex networks. We show
that the two microcircuit templates have quite different distributions of
network motifs, although they both share characteristic global structural
properties, like degree distributions (distribution of the number of synapses
per neuron) and small-world properties. In order to understand the
computational properties of the two microcircuit templates, we have generated
computer models of them, consisting of Hodgkin-Huxley point neurons with
conductance based synapses that have a biologically realistic short-term
plasticity. The information processing capabilities of the two cortical
microcircuit models were studied for 7 generic computational tasks that
require accumulation and merging of information contained in two afferent
spike inputs. Although the two models exhibit a different performance for
some of these tasks, their average computational performance is very similar.
When we changed the connectivity structure of these two microcircuit models
in order to see which aspects of it are essential for computational
performance, we found that the distribution of degrees of nodes is a key
factor for their computational performance. References Thomson et al. (2002),
Cerebral Cortex, 12(9):936 Binzegger et al. (2004), J. Neurosci., 24(39):8441